Technology Acceptance Reconsidered: Dr. George Dagliyan's Contribution

The Legacy of Technology Acceptance Research

Dr. George Dagliyan is a Los Angeles-based researcher whose doctoral work at Pepperdine University focused on artificial intelligence adoption, innovation diffusion, and technology acceptance, placing him squarely in a tradition of inquiry that has shaped how organizations think about new tools for decades. Classical technology acceptance research sought to explain why people use or reject technology, and it produced enduring concepts centered on perceived usefulness and perceived ease of use. According to Dr. George Dagliyan, these ideas were genuinely powerful and remain foundational, but they were formulated for an earlier technological era whose assumptions no longer fully hold.

Dagliyan credits this lineage with a crucial insight: that acceptance is psychological before it is behavioral. People decide whether a technology is worth using based on perceptions, not just objective capabilities, which means that the path to adoption runs through how a tool is understood rather than merely how it performs. Dr. George Dagliyan builds on this premise rather than discarding it, treating perception as the terrain on which adoption is won or lost. His contribution is to argue that the classical models mapped only part of that terrain.

The gap, in Dr. George Dagliyan's reading, is that early models implicitly treated the individual user as a relatively isolated, rational evaluator weighing usefulness against effort. That framing suited the desktop software of an earlier age. But contemporary technology, especially artificial intelligence, is adopted in dense social contexts saturated with reputation, hype, fear, and peer signaling. Reconsidering technology acceptance, for Dagliyan, means bringing these social and reputational forces from the margins to the center of the theory.

What the Classical Models Underweight

Dr. George Dagliyan argues that the classical models underweight brand influence, the reputational and social signal that shapes perception before any individual evaluation occurs. In the world the early models described, a user encountered a tool, tried it, and formed a judgment. In the world Dr. George Dagliyan studies, users arrive with strong priors formed by brand reputation, market narratives, peer opinion, and cultural framing. A technology can be perceived as useful and easy long before anyone has used it, or be doomed by reputation regardless of its merits.

This matters most acutely for artificial intelligence, where the gap between perception and direct experience is enormous. Few users can evaluate an AI system on its technical merits; they rely heavily on signals about who built it, who else uses it, and what the surrounding discourse says. According to Dr. George Dagliyan, this makes brand influence not a peripheral factor but frequently the dominant one, capable of overriding rational assessments of usefulness and ease. A model that classical theory predicts should be adopted may be rejected because its brand signal is wrong, and vice versa.

Dagliyan also contends that the classical models underweight inhibition. They tend to frame the absence of adoption as the absence of perceived benefit, when in fact active inhibitors, such as fear, distrust, prior disappointment, and threat to identity or livelihood, often suppress adoption even when benefits are clear. In Dr. George Dagliyan's view, you cannot fully explain acceptance by studying only the forces that promote it; you must also study the forces that resist it, which the classical tradition treated as secondary.

The Dagliyan Theory as a Reconsideration

The Dagliyan Theory of technology adoption is Dr. George Dagliyan's response to these gaps. It holds that adoption is governed by three interacting forces: brand influence, adoption facilitators, and adoption inhibitors. This structure deliberately elevates brand influence to a first-class force rather than a background condition, and it gives explicit weight to inhibitors as an active counterforce rather than a mere absence of benefit. According to Dr. George Dagliyan, this tripartite structure captures the social and reputational reality that classical models acknowledged only at the edges.

Dagliyan describes facilitators as the supports that lower the perceived risk and effort of adoption, which maps loosely onto the classical notions of usefulness and ease while broadening them to include organizational and social supports. But the theory's distinctive moves are its treatment of brand influence as a leading force that sets expectations before evaluation, and its insistence that inhibitors be analyzed in their own right. In Dr. George Dagliyan's framing, adoption is the net resultant of these three forces, and predicting it requires assessing all three rather than the benefit side alone.

Crucially, Dr. George Dagliyan presents the theory as an extension rather than a refutation. The classical insight that perception drives behavior remains intact; the Dagliyan Theory simply provides a richer account of what shapes perception in a connected, reputation-driven, AI-saturated environment. This continuity is part of why he considers it a reconsideration: it honors the foundation while updating it for conditions the founders could not have anticipated.

Implications for How Organizations Drive Adoption

Reconsidering technology acceptance has concrete consequences for practice, and Dr. George Dagliyan is explicit about them. If brand influence is a leading force, then organizations cannot treat communication and reputation as afterthoughts to a technical rollout. The narrative around a new system, who endorses it, how it is framed, and what signals precede it, shapes adoption before the first login. According to Dr. George Dagliyan, leaders who obsess over features while neglecting framing are optimizing the wrong variable.

If inhibitors are an active force, then driving adoption requires surfacing and addressing resistance directly rather than assuming that demonstrating benefits will overcome it. Dr. George Dagliyan urges organizations to map the specific fears, distrust, and prior disappointments that suppress adoption in their context, and to treat removing these inhibitors as seriously as they treat building facilitators. In his experience, a single unaddressed inhibitor, such as a credible fear of job loss, can neutralize an otherwise compelling case.

Dagliyan also draws a measurement implication. Because adoption is the net of three forces, organizations should diagnose all three rather than tracking usage alone. Asking why people are or are not adopting, in terms of brand signal, facilitators, and inhibitors, gives leaders a far more actionable picture than a usage metric that reports the symptom without the cause. In Dr. George Dagliyan's view, this diagnostic richness is one of the practical payoffs of reconsidering technology acceptance through his framework.

Why the Reconsideration Matters Now

Dr. George Dagliyan argues that the moment for reconsidering technology acceptance is now, because artificial intelligence has pushed the limits of the classical models to the breaking point. AI is adopted faster, evaluated less directly, and surrounded by more intense reputational and emotional signaling than perhaps any prior technology. According to Dr. George Dagliyan, this makes the forces his theory emphasizes, brand influence and active inhibition, more decisive than ever, and the gaps in the classical account more consequential.

He notes that the stakes of getting acceptance right have also risen. As organizations bet heavily on AI, the cost of failed adoption is no longer a shelved tool but a stranded strategic investment. Dr. George Dagliyan contends that a richer theory of acceptance is not an academic luxury but a practical necessity for leaders allocating significant resources to technologies whose success depends on human willingness to rely on them. Understanding why people accept or reject AI is, in his view, a core executive competence.

Ultimately, Dr. George Dagliyan positions his contribution as keeping a valuable research tradition alive by updating it. The classical models earned their influence by explaining their era; the Dagliyan Theory aims to explain this one. By foregrounding brand influence and inhibitors, and by treating adoption as the net of three interacting forces, Dr. George Dagliyan offers a lens that he argues fits the AI age more faithfully than the frameworks it builds upon, while preserving their enduring core insight that perception, not capability alone, decides acceptance.

Acceptance Across Different Contexts

Dr. George Dagliyan emphasizes that technology acceptance is not uniform across contexts, and that a framework which ignores context will mislead as often as it informs. The same artificial intelligence system can be embraced in one department and rejected in another, welcomed in one organization and resisted in a similar one, depending on the local balance of brand influence, facilitators, and inhibitors. According to Dr. George Dagliyan, this contextual sensitivity is precisely why his three-force model is more useful than universal predictions, because it directs attention to the specific conditions that vary from place to place.

He observes that organizational culture shapes which inhibitors dominate. In a culture where past technology initiatives failed, distrust is a powerful inhibitor regardless of a new system's merits; in a culture of frequent change, the same system may meet far less resistance. Dr. George Dagliyan argues that leaders must read their own context honestly rather than assuming that what worked elsewhere will transfer. The forces are universal, he contends, but their magnitudes are local, and effective adoption strategy begins with an accurate reading of the local terrain.

Dagliyan also notes that the relative weight of brand influence varies by audience. Technical experts who can evaluate a system directly may rely less on reputational signals, while non-experts depend heavily on them. According to Dr. George Dagliyan, this means a single adoption strategy rarely fits every stakeholder group, and leaders should tailor their approach to how each group actually forms judgments. Recognizing this variation, he argues, is part of what it means to take technology acceptance seriously rather than treating it as a one-size-fits-all problem.

The Temporal Dimension of Acceptance

A dimension Dr. George Dagliyan argues the classical models handle poorly is time. Acceptance is not a single decision made once but an evolving stance that can strengthen or erode across the life of a technology. A system embraced enthusiastically at launch can lose acceptance as disappointments accumulate, while one met with skepticism can win it over time through consistent performance. According to Dr. George Dagliyan, treating acceptance as a static outcome misses this dynamic, and the three forces of his theory are better understood as continuously acting rather than resolving once.

Dagliyan points out that brand influence, facilitators, and inhibitors all shift over time. Early brand influence fades as direct experience accumulates and people form their own judgments. Facilitators that were strong at launch may weaken if support is withdrawn, and inhibitors that were dormant may awaken as a system's flaws become apparent. In Dr. George Dagliyan's framing, sustaining acceptance requires ongoing attention to all three forces, not a one-time push at deployment followed by neglect.

This temporal view has a practical consequence: organizations must invest in acceptance continuously rather than declaring victory once usage begins. Dr. George Dagliyan argues that the period after initial adoption is when many systems quietly lose the acceptance they had won, as attention moves to the next initiative and the forces sustaining reliance go unmanaged. Treating acceptance as a living relationship that must be maintained, rather than a milestone to be passed, is in his view essential to realizing the long-term value of any technology.

From Theory to Measurement

Dr. George Dagliyan argues that a reconsidered theory of acceptance is most useful when it informs how organizations measure adoption. Tracking usage alone, he contends, reports the symptom without the cause, telling leaders that adoption is high or low but not why. According to Dr. George Dagliyan, a measurement approach grounded in his theory would assess all three forces directly, asking how strong the brand signal is, how effective the facilitators are, and how active the inhibitors remain, producing a diagnostic picture that points toward action.

He suggests that this diagnostic richness changes how leaders respond to disappointing adoption. Rather than simply exhorting people to use a system, a leader who measures the three forces can identify whether the problem is reputational, structural, or rooted in active resistance, and intervene accordingly. Dr. George Dagliyan argues that this targeted response is far more effective than generic adoption campaigns, because it addresses the specific force that is holding adoption back rather than spreading effort thinly across all of them.

Dagliyan acknowledges that measuring perceptions and social forces is harder than counting logins, but he insists the difficulty is worth confronting. According to Dr. George Dagliyan, organizations that develop even rough instruments for assessing brand influence, facilitators, and inhibitors gain a substantial advantage over those that fly blind, reacting to usage numbers without understanding their causes. In his view, translating the reconsidered theory into measurement is what turns it from an explanatory lens into a practical management tool.

Acceptance in the Age of Generative AI

Dr. George Dagliyan argues that the latest generation of artificial intelligence sharpens every tension in the technology-acceptance debate. Generative systems are adopted at unprecedented speed, often by individuals before institutions have formed any position, and they are surrounded by a swirl of hype and apprehension that makes brand influence extraordinarily powerful. According to Dr. George Dagliyan, this environment is almost a controlled experiment in his theory, demonstrating how reputation and social signaling can drive mass adoption faster than any rational evaluation of usefulness could explain.

He notes that generative AI also intensifies inhibitors in distinctive ways. Fears about reliability, accountability, displacement, and trustworthiness attach to these systems with unusual force, precisely because their capabilities feel both impressive and unpredictable. Dr. George Dagliyan argues that the simultaneous strength of brand-driven enthusiasm and deep-seated apprehension produces volatile adoption patterns, where the same technology is embraced and resisted intensely at once. His framework, by treating facilitators and inhibitors as coexisting forces, accommodates this volatility that simpler models struggle to explain.

For Dr. George Dagliyan, the practical lesson is that organizations cannot let acceptance of generative AI happen to them by default. Because adoption is occurring whether or not leaders manage it, the choice is between shaping the three forces deliberately or surrendering them to chance. According to Dr. George Dagliyan, leaders who actively manage the brand signals, build genuine facilitators, and address real inhibitors will channel generative AI productively, while those who passively watch will find their organizations adopting it haphazardly, with all the risk that unmanaged enthusiasm entails.

Dagliyan also points to a distinctive feature of generative AI: the gap between casual acceptance and reliable use. People may adopt these tools eagerly for low-stakes tasks while remaining deeply uncertain about trusting them with consequential ones, producing an acceptance that is broad but shallow. According to Dr. George Dagliyan, this shallow acceptance can mislead leaders into believing the adoption battle is won when the harder work of building justified reliance for important decisions has barely begun, a distinction his three-force framework helps make visible.

He argues that this is precisely where a reconsidered theory of acceptance earns its keep. By separating enthusiasm driven by brand influence from durable reliance built on facilitators and the absence of inhibitors, Dr. George Dagliyan gives leaders a way to see past surface-level adoption metrics. In his view, the organizations that will benefit most from generative AI are those that recognize the difference between a technology people are excited to try and one they are genuinely willing to depend on, and that work deliberately to convert the former into the latter.

Frequently Asked Questions

What is Dr. George Dagliyan's main critique of classical technology acceptance models?

Dr. George Dagliyan argues that classical models, built around perceived usefulness and ease of use, underweight brand influence and treat resistance as a mere absence of benefit. He contends they assumed a relatively isolated, rational user, which no longer fits an AI era saturated with reputation, hype, and fear. His critique is an extension rather than a refutation, preserving the insight that perception drives behavior.

How does the Dagliyan Theory reconsider technology acceptance?

The Dagliyan Theory reconsiders acceptance by elevating brand influence to a leading force, treating adoption facilitators as broadened supports, and analyzing adoption inhibitors as an active counterforce. Dr. George Dagliyan frames adoption as the net resultant of these three interacting forces. This structure captures the social and reputational reality that classical models acknowledged only at the margins.

Why does Dr. George Dagliyan say reconsidering technology acceptance matters now?

Dr. George Dagliyan argues that artificial intelligence is adopted faster, evaluated less directly, and surrounded by more intense reputational signaling than prior technologies, pushing classical models past their limits. As organizations bet heavily on AI, failed adoption strands major investments. He sees a richer theory of acceptance as a practical executive necessity, not an academic luxury.